In our network, \(C\)  was set to 32, \(N\)  was 25 for \(5\times 5\)  input LFs and 81 for \(9\times 9\)  input LFs. The network weights was initialized using the Kaiming method [1]} and optimized using the Adam method [2]} with \({\beta }_{1}=0.9\)  and \({\beta }_{2}=0.999\) . The reduction ratios \(\xi \)  and \(\theta \)  in attention blocks are set to 2. Our network was implemented in PyTorch on a PC with an NVidia RTX 2080Ti GPU. The batch size was set to 30. The learning rate was initially set to \(5\times 10^{-4}\) , and reduced to half after every 20 epochs. The training was stopped after 80 epochs since more epochs can not further introduce performance improvements.
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